Iterative Learning Control Strategy for a Furuta Pendulum System with Variable-Order Linearization

We consider Iterative Learning Control for the Furuta Pendulum nonlinear mechanical system, where goal is to learn input torque such that pendulum angle follows a reference. show linearization of considered system variable trajectory-dependent order and thus some existing solutions do not apply. propose novel method based on observability matrix inversion allowing deal with variable-order minimum realization. The applicability proposed illustrated simulations.